Currently, in the case of measuring a distance between an obstacle and a vehicle such as automobile, robot and unmanned aerial vehicle, a monocular vision technique has such detects as low accuracy, high missing rate and high misreporting rate, as compared with a radar distance-measurement technique and a binocular vision distance-measurement technique.
In the case of acquiring a speed of the obstacle relative to the vehicle, it is necessary to track and match depths of the obstacle measured twice, so as to acquire a correspondence between two pieces of sampling data of the same obstacle. For a conventional obstacle tracking algorithm, it is necessary to perform feature point matching and obstacle division on obstacle information acquired by two sampling operations. A feature point matching scheme, no matter on the basis of a depth map or an image, has such defects as large computation burden and low accuracy. Hence, it is impossible for the conventional algorithm to accurately determine the obstacle.